Methods and apparatus to determine an audience composition based on voice recognition

ABSTRACT

Methods, apparatus, systems and articles of manufacture are disclosed. An example apparatus includes a controller to cause a people meter to emit a prompt for input of audience identification information at a first time and determine a first audience count based on the input, an audio detector to determine a second audience count based on signatures generated from audio data captured in the media environment, and a comparator to cause the people meter to not emit the prompt for at least a first time period after the first time when the first audience count is equal to the second audience count.

RELATED APPLICATION(S)

This patent arises from a continuation of U.S. patent application Ser.No. 16/998,811, which is titled “METHODS AND APPARATUS TO DETERMINE ANAUDIENCE COMPOSITION BASED ON VOICE RECOGNITION,” and which was filed onAug. 20, 2020. Priority to U.S. patent application Ser. No. 16/998,811is claimed. U.S. patent application Ser. No. 16/998,811 is herebyincorporated herein by reference in its entirety.

FIELD OF THE DISCLOSURE

This disclosure relates generally to audience monitoring, and, moreparticularly, to methods and apparatus to determine an audiencecomposition based on voice recognition.

BACKGROUND

Media monitoring companies, also referred to as audience measuremententities, monitor user interaction with media devices, such assmartphones, tablets, laptops, smart televisions, etc. To facilitatesuch monitoring, monitoring companies enlist panelists and installmeters at the media presentation locations of those panelists. Themeters monitor media presentations and transmit media monitoringinformation to a central facility of the monitoring company. Such mediamonitoring information enables the media monitoring companies to, amongother things, monitor exposure to advertisements, determineadvertisement effectiveness, determine user behavior, identifypurchasing behavior associated with various demographics, etc.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example audience measurement system having anexample meter to monitor an example media presentation environment andgenerate exposure data for the media.

FIG. 2 illustrates a block diagram of the example meter of FIG. 1 .

FIG. 3 illustrates a block diagram of an example people meter includedin the example meter of FIG. 2 to determine an audience composition foraudience monitoring data.

FIG. 4 illustrates a block diagram of an example audience audio detectorincluded in the example people meter of FIG. 3 to identify audiencemembers.

FIG. 5 illustrates a block diagram of an example people identificationmodel controller included in the example people meter of FIG. 3 to traina model to determine an audience composition of the media presentationenvironment of FIG. 1 .

FIG. 6 is a flowchart representative of machine readable instructionswhich may be executed to implement the example audience audio detectorof FIGS. 3 and/or 4 to identify audience members.

FIG. 7 is a flowchart representative of machine readable instructionswhich may be executed to implement the example people meter of FIGS. 2,3, 4 , and/or 5 to verify an audience composition of the mediapresentation environment of FIG. 1 .

FIG. 8 is a flowchart representative of machine readable instructionswhich may be executed to implement the example people identificationmodel controller of FIGS. 3 and/or 5 to train the model to determine theaudience composition of the media presentation environment of FIG. 1 .

FIG. 9 illustrates a block diagram of an example processing platformstructured to execute the instructions of one or more of FIGS. 6-8 toimplement the example people meter of FIGS. 2-5 .

The figures are not to scale. In general, the same reference numberswill be used throughout the drawing(s) and accompanying writtendescription to refer to the same or like parts.

Unless specifically stated otherwise, descriptors such as “first,”“second,” “third,” etc. are used herein without imputing or otherwiseindicating any meaning of priority, physical order, arrangement in alist, and/or ordering in any way, but are merely used as labels and/orarbitrary names to distinguish elements for ease of understanding thedisclosed examples. In some examples, the descriptor “first” may be usedto refer to an element in the detailed description, while the sameelement may be referred to in a claim with a different descriptor suchas “second” or “third.” In such instances, it should be understood thatsuch descriptors are used merely for identifying those elementsdistinctly that might, for example, otherwise share a same name.

DETAILED DESCRIPTION

At least some meters that perform media monitoring, such as the metersdescribed above, implement media identification features and peopleidentification features. Such features (e.g., media identification andpeople identification) enable the generation of media monitoringinformation that can be used for determining audience exposure (alsoreferred to as user exposure) to advertisements, determiningadvertisement effectiveness, determining user behavior relative tomedia, identifying the purchasing behavior associated with variousdemographics, etc. The people identification features of the meterdetermine the audience in a media presentation environment. For example,the people identification feature may be implemented by active peoplemeters, passive people meters, and/or a combination of active peoplemeters and passive people meters to determine a people count.

An active people meter obtains a people count by actively prompting anaudience to enter information for audience member identification. Insome examples, an active people meter identifies an audience member bythe audience member's assigned panelist number or visitor number. Forexample, the active people meter obtains the assigned panelist number orvisitor number through a communication channel. In some examples, theactive people meter pairs the information corresponding to the audienceinput with a household (e.g., a specific media environment) and with thecorresponding demographic data for the people of that household. In someexamples, the active people meter validates viewership (e.g., the numberof audience members viewing media in the media environment) at a setinterval. For example, the active people meter generates a promptingmessage for the audience members to verify that they are still in theaudience. In this manner, the active people meter relies on audiencecompliance. In some examples, maintaining audience compliance is achallenge. For example, audience members may incorrectly enter thenumber of people viewing the media and/or they may miss the promptingmessages generated by the active people meter.

A passive people meter obtains audience information passively, usuallyby capturing images of the audience using a camera and then employingfacial recognition to identify the individual audience members includedin the audience. In some examples, image processing for facialrecognition can be processor intensive. Additionally, facial recognitionalgorithms can take a substantial amount of time to reliably recognizepeople in an image.

To enable an accurate and less invasive method of determining anaudience composition, example methods and apparatus disclosed hereinutilize a combination of a passive people meter and an active peoplemeter. As used herein, “audience composition” refers to the numberand/or identities of audience members in the audience. An examplepassive people meter disclosed herein includes an example audiodetection system to identify audience members in the media environment.In some examples, the audio detection system includes an audio sensor torecord the media environment and record samples of audio data. Theexample passive people meter utilizes the samples of audio data todetect and identify the audience members in the media environment.

Example methods and apparatus disclosed herein additionally utilize theactive people meter to identify the audience members in the mediaenvironment. An example people meter, implemented by the example activepeople meter and the example passive people meter as disclosed herein,includes an example audience audio detector to identify audiencemembers. In some examples, when the audience audio detector is unable toidentify one or more audience members, the audience audio detector cannotify the active people meter to generate a new prompting message forthe audience member(s).

In some examples, the example people meter reduces (e.g., minimizes) theamount of prompting generated by the active people meter relative toprior people meters that do not employ a combination of active andpassive people metering. For example, when the audience audio detectoridentifies the audience member(s), the audience composition is verified,and subsequent active prompting can be disabled for at least a givenmonitoring interval (e.g., 5 minutes, 15 minutes, etc.). Using audiosensing, the people meter monitors the media environment over time(e.g., continuously, at sampled time intervals, etc.) to determine thenumber of different speech patterns attributable to unique audiencemembers, validate against the number entered on the active people meterand/or previously logged, and accurately determine the audiencecomposition.

In some examples disclosed herein, the example people meter includes apeople identification model controller to train a model to learn aboutthe corresponding household media environment. As used herein, a modelis a description of an environment using mathematical concepts andlanguage. A model is generally composed of relationships and variables,the relationships describing operators (e.g., such as algebraicoperators, functions, etc.) and the variables describing monitoredenvironment parameters of interest that can be quantified. In someexamples, the model is a machine learning and/or artificial intelligence(AI) model such as a Linear Regression model, a decision tree, a supportvector machine (SVM) model, a Naïve Bayes model, etc.

In some examples, the people identification model controller obtainsdata from the comparator, the active people meter, and the passivepeople meter and generates a feature vector corresponding to the data.The example people identification model controller utilizes the featurevector to train the model. For example, the feature vector includes datarepresentative of descriptive characteristics of a physical environment(e.g., the household media environment). In some examples, such dataincludes a date and time, a number and/or identification of audiencemembers present in the media environment, a media source (e.g., radiomedia, television media, pay per view media, movies, Internet ProtocolTelevision (IPTV), satellite television (TV), Internet radio, satelliteradio, digital television), a media channel (e.g., broadcast channel, adomain name), and the demographics of the audience members. In thismanner, the example people identification model controller can generatethe model to learn who will be in the audience and at what time.Eventually, when training is complete, the model can be deployed at themeter and utilized to make informed decisions about the audiencecomposition.

In some examples, the model can utilize the identification of audiencemembers determined by the audience audio detector as a metric todetermine whether the people meter is actually crediting the mediaexposure to the correct audience members. For example, the model couldbe used to determine if the audience views the same or similar mediaevery Tuesday night at 8:00 pm, if there are usually two particularpeople present in the media audience, etc. In such an example, the modelis used to verify the accuracy of the audience composition based on theinformation obtained.

FIG. 1 is an illustration of an example audience measurement system 100having an example meter 102 to monitor an example media presentationenvironment 104. In the illustrated example of FIG. 1 , the mediapresentation environment 104 includes panelists 106, 107, and 108, anexample media device 110 that receives media from an example mediasource 112, and the meter 102. The meter 102 identifies the mediapresented by the media device 110 and reports media monitoringinformation to an example central facility 114 of an audiencemeasurement entity via an example gateway 116 and an example network118. The example meter 102 of FIG. 1 sends media monitoring data and/oraudience monitoring data to the central facility 114 periodically,a-periodically and/or upon request by the central facility 114.

In the illustrated example of FIG. 1 , the media presentationenvironment 104 is a room of a household (e.g., a room in a home of apanelist, such as the home of a “Nielsen family”) that has beenstatistically selected to develop media (e.g., television) ratings datafor a population/demographic of interest. In the illustrated example ofFIG. 1 , the example panelists 106, 107, and 108 of the household havebeen statistically selected to develop media ratings data (e.g.,television ratings data) for a population/demographic of interest.People become panelists via, for example, a user interface presented ona media device (e.g., via the media device 110, via a website, etc.).People become panelists in additional or alternative manners such as,for example, via a telephone interview, by completing an online survey,etc. Additionally or alternatively, people may be contacted and/orenlisted using any desired methodology (e.g., random selection,statistical selection, phone solicitations, Internet advertisements,surveys, advertisements in shopping malls, product packaging, etc.). Insome examples, an entire family may be enrolled as a household ofpanelists. That is, while a mother, a father, a son, and a daughter mayeach be identified as individual panelists, their viewing activitiestypically occur within the family's household.

In the illustrated example, one or more panelists 106, 107, and 108 ofthe household have registered with an audience measurement entity (e.g.,by agreeing to be a panelist) and have provided their demographicinformation to the audience measurement entity as part of a registrationprocess to enable associating demographics with media exposureactivities (e.g., television exposure, radio exposure, Internetexposure, etc.). The demographic data includes, for example, age,gender, income level, educational level, marital status, geographiclocation, race, etc., of a panelist. While the example mediapresentation environment 104 is a household, the example mediapresentation environment 104 can additionally or alternatively be anyother type(s) of environments such as, for example, a theater, arestaurant, a tavern, a retail location, an arena, etc.

In the illustrated example of FIG. 1 , the example media device 110 is atelevision. However, the example media device 110 can correspond to anytype of audio, video, and/or multimedia presentation device capable ofpresenting media audibly and/or visually. In some examples, the mediadevice 110 (e.g., a television) may communicate audio to another mediapresentation device (e.g., an audio/video receiver) for output by one ormore speakers (e.g., surround sound speakers, a sound bar, etc.). Asanother example, the media device 110 can correspond to a multimediacomputer system, a personal digital assistant, a cellular/mobilesmartphone, a radio, a home theater system, stored audio and/or videoplayed back from a memory such as a digital video recorder or a digitalversatile disc, a webpage, and/or any other communication device capableof presenting media to an audience (e.g., the panelists 106, 107, and108).

The media source 112 may be any type of media provider(s), such as, butnot limited to, a cable media service provider, a radio frequency (RF)media provider, an Internet based provider (e.g., IPTV), a satellitemedia service provider, etc. The media may be radio media, televisionmedia, pay per view media, movies, Internet Protocol Television (IPTV),satellite television (TV), Internet radio, satellite radio, digitaltelevision, digital radio, stored media (e.g., a compact disk (CD), aDigital Versatile Disk (DVD), a Blu-ray disk, etc.), any other type(s)of broadcast, multicast and/or unicast medium, audio and/or video mediapresented (e.g., streamed) via the Internet, a video game, targetedbroadcast, satellite broadcast, video on demand, etc.

The example media device 110 of the illustrated example shown in FIG. 1is a device that receives media from the media source 112 forpresentation. In some examples, the media device 110 is capable ofdirectly presenting media (e.g., via a display) while, in otherexamples, the media device 110 presents the media on separate mediapresentation equipment (e.g., speakers, a display, etc.). Thus, as usedherein, “media devices” may or may not be able to present media withoutassistance from a second device. Media devices are typically consumerelectronics. For example, the media device 110 of the illustratedexample could be a personal computer such as a laptop computer, and,thus, capable of directly presenting media (e.g., via an integratedand/or connected display and speakers). In some examples, the mediadevice 110 can correspond to a television and/or display device thatsupports the National Television Standards Committee (NTSC) standard,the Phase Alternating Line (PAL) standard, the Système Électronique pourCouleur avec Mémoire (SECAM) standard, a standard developed by theAdvanced Television Systems Committee (ATSC), such as high definitiontelevision (HDTV), a standard developed by the Digital VideoBroadcasting (DVB) Project, etc. Advertising, such as an advertisementand/or a preview of other programming that is or will be offered by themedia source 112, etc., is also typically included in the media. While atelevision is shown in the illustrated example, any other type(s) and/ornumber(s) of media device(s) may additionally or alternatively be used.For example, Internet-enabled mobile handsets (e.g., a smartphone, aniPod®, etc.), video game consoles (e.g., Xbox®, PlayStation 3, etc.),tablet computers (e.g., an iPad®, a Motorola™ Xoom™, etc.), digitalmedia players (e.g., a Roku® media player, a Slingbox®, a Tivo®, etc.),smart televisions, desktop computers, laptop computers, servers, etc.may additionally or alternatively be used.

The example meter 102 detects exposure to media and electronicallystores monitoring information (e.g., a code/watermark detected with thepresented media, a signature of the presented media, an identifier of apanelist present at the time of the presentation, a timestamp of thetime of the presentation) of the presented media. The stored monitoringinformation is then transmitted back to the central facility 114 via thegateway 116 and the network 118. While the media monitoring informationis transmitted by electronic transmission in the illustrated example ofFIG. 1 , the media monitoring information may additionally oralternatively be transferred in any other manner, such as, for example,by physically mailing the meter 102, by physically mailing a memory ofthe meter 102, etc.

The meter 102 of the illustrated example of FIG. 1 combines mediameasurement data and people metering data. For example, mediameasurement data is determined by monitoring media output by the mediadevice 110 and/or other media presentation device(s), and peoplemetering data (also referred to as demographic data, people monitoringdata, etc.) is determined by monitoring people with to the meter 102.Thus, the example meter 102 provides dual functionality of a media meterto collect content media data and people meter to collect and/orassociate demographic information corresponding to the collected mediameasurement data.

For example, the meter 102 of the illustrated example collects mediaidentifying information and/or data (e.g., signature(s), fingerprint(s),code(s), tuned channel identification information, time of exposureinformation, etc.) and people data (e.g., user identifiers, demographicdata associated with audience members, etc.). The media identifyinginformation and the people data can be combined to generate, forexample, media exposure data (e.g., ratings data) indicative ofamount(s) and/or type(s) of people that were exposed to specificpiece(s) of media distributed via the media device 110. To extract mediaidentification data, the meter 102 and/or the example audiencemeasurement system 100 extracts and/or processes the collected mediaidentifying information and/or data received by the meter 102, which canbe compared to reference data to perform source and/or contentidentification. Any other type(s) and/or number of media monitoringtechniques can be supported by the meter 102.

Depending on the type(s) of metering the meter 102 is to perform, themeter 102 can be physically coupled to the media device 110 or may beconfigured to capture signals emitted externally by the media device 110(e.g., free field audio) such that direct physical coupling to the mediadevice 110 is not required. For example, the meter 102 of theillustrated example may employ non-invasive monitoring not involving anyphysical connection to the media device 110 (e.g., via Bluetooth®connection, WIFI® connection, acoustic watermarking, etc.) and/orinvasive monitoring involving one or more physical connections to themedia device 110 (e.g., via USB connection, a High Definition MediaInterface (HDMI) connection, an Ethernet cable connection, etc.).

In examples disclosed herein, to monitor media presented by the mediadevice 110, the meter 102 of the illustrated example employs audiowatermarking techniques and/or signature based-metering techniques.Audio watermarking is a technique used to identify media, such astelevision broadcasts, radio broadcasts, advertisements (televisionand/or radio), downloaded media, streaming media, prepackaged media,etc. Existing audio watermarking techniques identify media by embeddingone or more audio codes (e.g., one or more watermarks), such as mediaidentifying information and/or an identifier that may be mapped to mediaidentifying information, into an audio and/or video component of themedia. In some examples, the audio or video component is selected tohave a signal characteristic sufficient to hide the watermark. As usedherein, the terms “code” and “watermark” are used interchangeably andare defined to mean any identification information (e.g., an identifier)that may be inserted or embedded in the audio or video of media (e.g., aprogram or advertisement) for the purpose of identifying the media orfor another purpose such as tuning (e.g., a packet identifying header).As used herein “media” refers to audio and/or visual (still or moving)content and/or advertisements. To identify watermarked media, thewatermark(s) are extracted and used to access a table of referencewatermarks that are mapped to media identifying information.

Unlike media monitoring techniques based on codes and/or watermarksincluded with and/or embedded in the monitored media, fingerprint orsignature-based media monitoring techniques generally use one or moreinherent characteristics of the monitored media during a monitoring timeinterval to generate a substantially unique proxy for the media. Such aproxy is referred to as a signature or fingerprint, and can take anyform (e.g., a series of digital values, a waveform, etc.) representativeof any aspect(s) of the media signal(s) (e.g., the audio and/or videosignals forming the media presentation being monitored). A signature maybe a series of signatures collected in series over a timer interval. Agood signature is repeatable when processing the same mediapresentation, but is unique relative to other (e.g., different)presentations of other (e.g., different) media. Accordingly, the term“fingerprint” and “signature” are used interchangeably herein and aredefined herein to mean a proxy for identifying media that is generatedfrom one or more inherent characteristics of the media.

Signature-based media monitoring generally involves determining (e.g.,generating and/or collecting) signature(s) representative of a mediasignal (e.g., an audio signal and/or a video signal) output by amonitored media device and comparing the monitored signature(s) to oneor more references signatures corresponding to known (e.g., reference)media sources. Various comparison criteria, such as a cross-correlationvalue, a Hamming distance, etc., can be evaluated to determine whether amonitored signature matches a particular reference signature. When amatch between the monitored signature and one of the referencesignatures is found, the monitored media can be identified ascorresponding to the particular reference media represented by thereference signature that with matched the monitored signature. Becauseattributes, such as an identifier of the media, a presentation time, abroadcast channel, etc., are collected for the reference signature,these attributes may then be associated with the monitored media whosemonitored signature matched the reference signature. Example systems foridentifying media based on codes and/or signatures are long known andwere first disclosed in Thomas, U.S. Pat. No. 5,481,294, which is herebyincorporated by reference in its entirety.

For example, the meter 102 of the illustrated example senses audio(e.g., acoustic signals or ambient audio) output (e.g., emitted) by themedia device 110. For example, the meter 102 processes the signalsobtained from the media device 110 to detect media and/or sourceidentifying signals (e.g., audio watermarks) embedded in portion(s)(e.g., audio portions) of the media presented by the media device 110.To sense ambient audio output by the media device 110, the meter 102 ofthe illustrated example includes an audio sensor (e.g., a microphone).In some examples, the meter 102 may process audio signals obtained fromthe media device 110 via a direct cable connection to detect mediaand/or source identifying audio watermarks embedded in such audiosignals. In some examples, the meter 102 may process audio signalsand/or video signals to generate respective audio and/or videosignatures from the media presented by the media device 110.

To generate exposure data for the media, identification(s) of media towhich the audience is exposed are correlated with people data (e.g.,presence information) collected by the meter 102. The meter 102 of theillustrated example collects inputs (e.g., audience monitoring data)representative of the identities of the audience member(s) (e.g., thepanelists 106, 107, and 108). In some examples, the meter 102 collectsaudience monitoring data by periodically or a-periodically promptingaudience members in the monitored media presentation environment 104 toidentify themselves as present in the audience (e.g., audienceidentification information). In some examples, the meter 102 responds toevents (e.g., when the media device 110 is turned on, a channel ischanged, an infrared control signal is detected, etc.) by prompting theaudience member(s) to self-identify.

In some examples, the meter 102 determines an audience composition byutilizing the audio sensor 120. For example, the audio sensor 120records samples of audio data associated with the audience members(e.g., the panelists 106, 107, 108) of the media presentationenvironment 104 and provides the audio data to the meter 102 to detectone or more speech patterns. In some examples, the meter 102 responds toevents (e.g., when the media device 110 is turned on, a channel ischanged, an infrared control signal is detected, etc.) by prompting theaudio sensor 120 to record audio samples.

The example audio sensor 120 of the illustrated example of FIG. 1 is amicrophone. The example audio sensor 120 receives ambient sound (e.g.,free field audio) including audible media and/or sounds from audiencemembers in the vicinity of the meter 102. Additionally or alternatively,the example audio sensor 120 may be implemented by a line inputconnection. The line input connection may allow an external microphoneto be used with the meter 102 and/or, in some examples, may enable theaudio sensor 120 to be directly connected to an output of a media device110 (e.g., an auxiliary output of a television, an auxiliary output ofan audio/video receiver of a home entertainment system, etc.).

In some examples, the meter 102 is positioned in a location such thatthe audio sensor 120 receives ambient audio produced by the televisionand/or other devices of the media presentation environment 104 (FIG. 1 )with sufficient quality to identify media presented by the media device110 and/or other devices of the media presentation environment 104(e.g., a surround sound speaker system). For example, in examplesdisclosed herein, the meter 102 may be placed on top of the television,secured to the bottom of the television, etc. In some examples, theaudio sensor 120 also receives audio produced by audience members (e.g.,the panelists 106, 107, 108) of the media presentation environment 104with sufficient quality to identify speech patterns of the audiencemembers. While the illustrated example of FIG. 1 includes the audiosensor 120, examples disclosed herein can additionally or alternativelyuse multiple audio sensors. For example, one or more audio sensors canbe positioned to receive audio from the media device, and one or moreaudio sensors can be positioned to receive audio from a location inwhich an audience is expected to be present (e.g., an expected distancecorresponding to a couch, etc.). In such an example, audio sensor(s)positioned to receive audio from the media device are positionedrelatively closer to the media device and the audio sensor(s) positionedto receive audio from the audience members are positioned relativelycloser to the expected location of the audience members.

The audience monitoring data and the exposure data can then be compiledwith the demographic data collected from audience members such as, forexample, the panelists 106, 107, and 108 during registration to developmetrics reflecting, for example, the demographic composition of theaudience. The demographic data includes, for example, age, gender,income level, educational level, marital status, geographic location,race, etc., of the panelist. In some examples, the registration ofpanelists includes recording samples of audio data for each member ofthe household. For example, the meter 102 records the panelists 106,107, 108 saying one or more phrases. In examples disclosed herein, therecordings are used as reference samples of the audience members. Insome examples, the meter 102 generates and stores reference audiencesignatures of the recorded reference samples. That is, the referenceaudience signatures can be stored locally at the meter 102, stored atthe central facility 114 for signature matching, etc.

The example image sensor 125 of the illustrated example of FIG. 1 is acamera. The example image sensor 125 receives light waves, such as thelight waves emitting from the example media device 110 and converts theminto signals that convey information. Additionally or alternatively, theexample image sensor 125 may be implemented by a line input connection,where the video and images presented by the example media device 110 arecarried over an audio/video network (e.g., HDMI cable) to the examplemeter 102. The example image sensor 125 may not be included in theexample meter 102. For example, it may not be necessary for the meter102 to utilize the image sensor 125 to identify media data. However, insome examples, the image sensor 125 can be utilized for detection ofmedia data.

In some examples, the meter 102 may be configured to receive audienceinformation via an example input device 122 such as, for example, aremote control, an Apple iPad®, a cell phone, etc. In such examples, themeter 102 prompts the audience members to indicate their presence bypressing an appropriate input key on the input device 122. For example,the input device 122 may enable the audience member(s) (e.g., thepanelists 106, 107, and 108 of FIG. 1 ) and/or an unregistered user(e.g., a visitor to a panelist household) to input information to themeter 102 of FIG. 1 . This information includes registration data toconfigure the meter 102 and/or demographic data to identify the audiencemember(s). For example, the input device 122 may include a gender inputinterface, an age input interface, and a panelist identification inputinterface, etc. Although FIG. 1 illustrates multiple input devices 122,the example media presentation environment 104 may include an inputdevice 122 with multiple inputs for multiple audience members. Forexample, an input device 122 can be utilized as a household input device122 where panelists of the household may each have a corresponding inputassigned to them.

The example gateway 116 of the illustrated example of FIG. 1 is a routerthat enables the meter 102 and/or other devices in the mediapresentation environment (e.g., the media device 110) to communicatewith the network 118 (e.g., the Internet.)

In some examples, the example gateway 116 facilitates delivery of mediafrom the media source 112 to the media device 110 via the Internet. Insome examples, the example gateway 116 includes gateway functionality,such as modem capabilities. In some other examples, the example gateway116 is implemented in two or more devices (e.g., a router, a modem, aswitch, a firewall, etc.). The gateway 116 of the illustrated examplemay communicate with the network 118 via Ethernet, a digital subscriberline (DSL), a telephone line, a coaxial cable, a USB connection, aBluetooth connection, any wireless connection, etc.

In some examples, the example gateway 116 hosts a Local Area Network(LAN) for the media presentation environment 104. In the illustratedexample, the LAN is a wireless local area network (WLAN), and allows themeter 102, the media device 110, etc. to transmit and/or receive datavia the Internet. Alternatively, the gateway 116 may be coupled to sucha LAN. In some examples, the gateway 116 may be implemented with theexample meter 102 disclosed herein. In some examples, the gateway 116may not be provided. In some such examples, the meter 102 maycommunicate with the central facility 114 via cellular communication(e.g., the meter 102 may employ a built-in cellular modem).

The network 118 of the illustrated example is a wide area network (WAN)such as the Internet. However, in some examples, local networks mayadditionally or alternatively be used. Moreover, the example network 118may be implemented using any type of public or private network, such as,but not limited to, the Internet, a telephone network, a local areanetwork (LAN), a cable network, and/or a wireless network, or anycombination thereof.

The central facility 114 of the illustrated example is implemented byone or more servers. The central facility 114 processes and stores datareceived from the meter 102. For example, the example central facility114 of FIG. 1 combines audience monitoring data and programidentification data from multiple households to generate aggregatedmedia monitoring information. The central facility 114 generates reportsfor advertisers, program producers and/or other interested parties basedon the compiled statistical data. Such reports include extrapolationsabout the size and demographic composition of audiences of content,channels and/or advertisements based on the demographics and behavior ofthe monitored panelists.

As noted above, the meter 102 of the illustrated example provides acombination of media (e.g., content) metering and people metering. Theexample meter 102 of FIG. 1 is a stationary device that may be disposedon or near the media device 110. The meter 102 of FIG. 1 includes itsown housing, processor, memory and/or software to perform the desiredaudience measurement and/or people monitoring functions.

In examples disclosed herein, an audience measurement entity providesthe meter 102 to the panelist 106, 107, and 108 (or household ofpanelists) such that the meter 102 may be installed by the panelist 106,107 and 108 by simply powering the meter 102 and placing the meter 102in the media presentation environment 104 and/or near the media device110 (e.g., near a television set). In some examples, more complexinstallation activities may be performed such as, for example, affixingthe meter 102 to the media device 110, electronically connecting themeter 102 to the media device 110, etc.

To identify and/or confirm the presence of a panelist present in themedia device 110, the example meter 102 of the illustrated exampleincludes an example display 132. For example, the display 132 providesidentification of the panelists 106, 107, 108 present in the mediapresentation environment 104. For example, in the illustrated example,the meter 102 displays indicia or visual indicators (e.g., illuminatednumerals 1, 2 and 3) identifying and/or confirming the presence of thefirst panelist 106, the second panelist 107, and the third panelist 108.

FIG. 2 illustrates a block diagram of the example meter 102 of FIG. 1 togenerate exposure data for the media. The example meter 102 of FIG. 2 iscoupled to the example audio sensor 120 of FIG. 1 to determine audiencecomposition based on samples of audio data. The example meter 102 ofFIG. 2 includes an example media identifier 204, an example networkcommunicator 206, an example communication processor 208, an examplepeople meter 210, an example media measurement data controller 212, andan example data store 214.

The example media identifier 204 of the illustrated example of FIG. 2analyzes signals received via the image sensor 201 and/or audio receivedvia the audio sensor 120 and identifies the media being presented. Theexample media identifier 204 of the illustrated example outputs anidentifier of the media (e.g., media-identifying information) to themedia measurement data controller 212. In some examples, the mediaidentifier 204 utilizes audio and/or video watermarking techniques toidentify the media. Additionally or alternatively, the media identifier204 utilizes signature-based media identification techniques. Forexample, the media identifier 204 generates one or more signatures ofthe audio received from the audio sensor 120. As described above, themeter 102 may include one or more audio sensors. In such an example, themedia identifier 204 may generate one or more signatures of the audioreceived from the audio sensor that is relatively closer to the mediadevice (e.g., the media device 110). In some examples, the mediaidentifier 204 outputs generated signatures of the audio data to theexample people meter 210.

The example network communicator 206 of the illustrated example of FIG.2 is a communication interface configured to receive and/or otherwisetransmit corresponding communications to and/or from the centralfacility 114. In the illustrated example, the network communicator 206facilitates wired communication via an Ethernet network hosted by theexample gateway 116 of FIG. 1 . In some examples, the networkcommunicator 206 is implemented by a Wi-Fi radio that communicates viathe LAN hosted by the example gateway 116. In other examples disclosedherein, any other type of wireless transceiver may additionally oralternatively be used to implement the network communicator 206. Inexamples disclosed herein, the example network communicator 206communicates information to the communication processor 208 whichperforms actions based on the received information. In other examplesdisclosed herein, the network communicator 206 may transmit mediameasurement information provided by the media measurement datacontroller 212 (e.g., data stored in the data store 214) to the centralfacility 114 of the media presentation environment 104.

The example communication processor 208 of the illustrated example ofFIG. 2 receives information from the network communicator 206 andperforms actions based on that received information. For example, thecommunication processor 208 packages records corresponding to collectedexposure data and transmits records to the central facility 114. Inexamples disclosed herein, the communication processor 208 communicateswith the people meter 210 and/or a media measurement data controller 212to transmit people count data, demographic data, etc., to the networkcommunicator 206.

The example people meter 210 of the illustrated example of FIG. 2determines audience monitoring data representative of the number and/oridentities of the audience member(s) (e.g., panelists) present in themedia presentation environment 104. In the illustrated example, thepeople meter 210 is coupled to the audio sensor 120. In some examples,the people meter 210 is coupled to the audio sensor 120 via a directconnection (e.g., Ethernet) or indirect communication through one ormore intermediary components. In some examples, the audio sensor 120 isincluded in (e.g., integrated with) the meter 102. The example peoplemeter 210 collects data from the example audio sensor 120, the examplemedia identifier 204, and data from example input device(s) 122corresponding to audience monitoring data. In some examples, the meter102 includes one or more audio sensors. In such examples, the peoplemeter 210 obtains audio data from the audio sensors that are relativelycloser to the expected location of audience members. The people meter210 may then generate signatures of the audio data. In some examples,the people meter 210 and the media identifier 204 generate signaturesusing the same technique (e.g., generate unhashed signatures, generatehashed signatures, etc.). Additionally or alternatively, the peoplemeter 210 and the media identifier 204 can generate signatures usingdifferent techniques. The example people meter 210 provides the audiencemonitoring data to the media measurement data controller 212 such thatthe audience monitoring data can be correlated with the mediaidentification data to facilitate an identification of which media waspresented to which audience member (e.g., exposure data). The examplepeople meter 210 is described in further detail below in connection withFIGS. 3, 4, and 5 .

The example media measurement data controller 212 of the illustratedexample of FIG. 2 receives media identifying information (e.g., a code,a signature, etc.) from the media identifier 204 and audience monitoringdata from the people meter 210 and stores the received information inthe data store 214. The example media measurement data controller 212periodically and/or a-periodically transmits, via the networkcommunicator 206, the media measurement information stored in the datastore 214 to the central facility 114 for post-processing of mediameasurement data, aggregation and/or preparation of media monitoringreports. In some examples, the media measurement data controller 212generates exposure data. For example, the media measurement datacontroller 212 correlates the media identifying information withaudience monitoring data, as described above, to generate exposure data.

The example data store 214 of the illustrated example of FIG. 2 may beimplemented by any device for storing data such as, for example, flashmemory, magnetic media, optical media, etc. Furthermore, the data storedin the example data store 214 may be in any data format such as, forexample, binary data, comma delimited data, tab delimited data,structured query language (SQL) structures, etc. In the illustratedexample, the example data store 214 stores media identifying informationcollected by the media identifier 204 and audience monitoring datacollected by the people meter 210.

FIG. 3 illustrates a block diagram of the example people meter 210 ofFIG. 2 , which is to determine an audience composition for audiencemonitoring data in accordance with teachings of this disclosure. Theexample people meter 210 includes an example people meter controller302, an example interface 304, an example audience audio detector 305,an example comparator 308, an example people identification modelcontroller 310, and an example model database 312.

The example people meter controller 302 of the illustrated example ofFIG. 3 obtains user input from example input device(s) 122. For example,the panelists 106, 107, and 108 may press a key on the respective inputdevice(s) 122 indicating they are in the room (e.g., the mediapresentation environment 104). In some examples, the panelists 106, 107,108 enter a username, password, and/or other information to indicate whothey are and that they are in the room viewing media. In some examples,the people meter controller 302 determines a people count based on theuser input obtained from the input device(s) 122. For example, thepeople meter controller 302 may determine that two people (e.g., two ofthe panelists 106, 107, 108) have indicated their presence in the mediapresentation environment 104. In some examples, the people metercontroller 302 determines a people count in response to the media device110 turning on. The example people meter controller 302 provides thepeople count to the example comparator 308.

In some examples, the people meter controller 302 generates promptingmessages at periodic and/or aperiodic scheduling intervals. For example,the people meter controller 302 generates a prompting message to bedisplayed on the media device 110 and/or a display of the meter 102. Theprompting messages can include questions and/or requests to which thepanelists 106, 107, 108 are to respond. For example, the people metercontroller 302 may generate a prompting message every 42 minutes (or atsome other interval) asking the panelists 106, 107, 108 if they arestill viewing the media. In some examples, the people meter controller302 generates a prompting message based on one or more events, such aswhen the media device 110 is turned on, when the channel has changed,when the media source has changed, etc. In some examples, the peoplemeter controller 302 receives a trigger from the audience audio detector305 to determine whether to generate prompting messages or not generateprompting messages. The example people meter controller 302 communicatesthe prompting messages through the communication processor 208 of FIG. 2to the media device 110.

The example interface 304 of the illustrated example of FIG. 3communicates between the example audio sensor 120 and the exampleaudience audio detector 305. For example, the interface 304 obtains datafrom the audio sensor 120 and provides the data to the example audienceaudio detector 305. In some examples, the interface 304 obtains requestsfrom the example audience audio detector 305 and passes the requests tothe example audio sensor 120. For example, the interface 304 enablescommunication between the audio sensor 120 and the audience audiodetector 305. In some examples, the interface 304 enables communicationbetween the media identifier 204 and the audience audio detector 305.For example, the interface 304 obtains data (e.g., signatures) from themedia identifier 204 and provides the data to the example audience audiodetector 305. The example interface 304 may be any type of interface,such as a network interface card (NIC), an analog-to-digital converter,a digital-to-analog converter, Universal Serial Bus (USB), GigE,FireWire, Camera Link®, etc.

The example audience audio detector 305 of the illustrated example ofFIG. 3 obtains audio data from the example interface 304 and determinesthe audience composition based on the audio data. For example, theaudience audio detector 305 obtains samples of audio data from the audiosensor 120 via the interface 304 and analyzes the samples. For example,the audience audio detector can obtain audio data from the audio sensor120 and/or the audio sensor closer to the expected location of theaudience (e.g., the meter 102 includes multiple audio sensors). In someexamples, the audience audio detector 305 includes a signature generator(not illustrated) to generate one or more signatures of the audio data(e.g., using the same signature generating technique as the mediaidentifier 204 (FIG. 2 ), using a different signature generatingtechnique as the media identifier 204, etc.).

Additionally or alternatively, the audience audio detector 305 obtainssignatures of the audio data from the media identifier 204 via theinterface 304. That is, in such an example, the audience audio detector305 may not include a signature generator and instead obtains signaturesgenerated by the media identifier 204. In some examples, the audienceaudio detector 305 identifies one or more distinct speech patterns inthe signatures. The example audience audio detector 305 determines apeople count based on the number of distinct speech patterns. In someexamples, the audience audio detector 305 analyzes the detectedsignatures in comparison to the reference audience signatures toidentify audience member(s) (e.g., the voice(s) of the audiencemember(s)). The example audience audio detector 305 identifies audiencemembers and provides the identification to the example comparator 308,the example people identification model controller 310, and/or theexample people meter controller 302.

In some examples, the audience audio detector 305 is unable to identifythe person in the generated signature corresponding to a detected speechpattern. For example, the person corresponding to the speech pattern inthe generated signature may be a visitor, a distant relative of thepanelist, etc. In such an example, the audience audio detector 305triggers the people meter controller 302 to generate a prompting messagefor additional member logging. As used herein, member logging occurswhen a given audience member logs into their respective input device 122(or into a common input device 122 used in the environment) to indicatethat they are viewing the media. The example people meter controller 302generates the prompting message in an effort to obtain a response toverify the audience composition and generate accurate audiencemonitoring data. The example audience audio detector 305 is described infurther detail below in connection with FIG. 4 .

The example comparator 308 of the illustrated example of FIG. 3 obtainsthe people count at time t and the people count at time t−1 from theexample audience audio detector 305 and compares the two count values.That is, the comparator 308 compares the people count at a first time(e.g., time t) and the people count at a relatively earlier time (e.g.,time t−1). The example comparator 308 determines if the count values areequal in value. When the comparator 308 determines the count valuesmatch, then the people count is verified. In some examples, when thepeople count is verified, the example comparator 308 notifies theexample people meter controller 302 to reset a scheduling interval timer(e.g., a counter, clock, or other timing mechanisms) that initiate thegeneration of prompting messages. For example, if the people metercontroller 302 is configured to generate prompting messages every 42minutes, then a timer is set to 42 minutes. When the timer expires, aprompting message is triggered. However, if the comparator 308 verifiesthe number of people (e.g., the panelists 106, 107, 108) in the mediapresentation environment 104, a prompting message is not needed todetermine whether people are still viewing the media.

In some examples, the comparator 308 determines the two people countvalues are not equal. For example, there may only be one people count(e.g., audio sampling was not previously active). Thus, the comparator308 determines the people count does not match (e.g., compared to apeople count of zero corresponding to an inactive audio sensor 120 (FIG.1 )). In some examples, the people counts do not match due to anaudience member leaving the media presentation environment 104, anaudience member joining the media presentation environment 104, etc. Insuch examples, the example comparator 308 triggers the audience audiodetector 305 to identify the audience member(s) corresponding to theidentified speech patterns.

The example comparator 308 provides the people count to the examplepeople identification model controller 310. In some examples, thecomparator 308 determines a time of the comparison between the peoplecounts and provides the time to the people identification modelcontroller 310. For example, the comparator 308 may identify 4:58 pm asthe time corresponding to the comparison between the people counts. Insome examples, the comparator 308 updates, or otherwise trains, thepeople identification model when the comparator 308 provides the peoplecount and the time corresponding to the comparison to the peopleidentification model controller 310.

The example people identification model controller 310 of theillustrated example of FIG. 3 trains a people identification model basedon data obtained from the example comparator 308, the example peoplemeter controller 302, and the example audience audio detector 305. Insome examples, the people identification model controller 310 is incommunication with the example media measurement data controller 212 ofFIG. 2 . For example, the people identification model controller 310obtains information from the media measurement data controller 212 andprovides information to the media measurement data controller 212. Insome examples, the data obtained from the media measurement datacontroller 212 by the people identification model controller 310includes media identifying information. For example, the peopleidentification model controller 310 queries the media measurement datacontroller 212 for media identifying information at the timecorresponding to the comparison of people counts and/or the timecorresponding to the identification of audience members. In someexamples, the media measurement data controller 212 provides the data tothe people identification model controller 310 without receiving arequest and/or query. In this manner, the people identification modelcontroller 310 obtains the audience composition at, for example, 4:58 pmand obtains the broadcast channel airing on the media device 110 at 4:58pm. In some examples, the people identification model controller 310utilizes the data from one or more of the people meter controller 302,the audience audio detector 305, the comparator 308, and the mediameasurement data controller 212 to train a model to predict a verifiedaudience composition for particular dates and times.

In some examples, the people identification model controller 310 passesthe verified audience composition to the media measurement datacontroller 212. For example, the people identification model controller310 obtains the verified audience composition, packages the information,and provides the information to the media measurement data controller212 for generation of exposure data. For example, the media measurementdata controller 212 utilizes the information to correlate the verifiedaudience composition with the media identifying information. In someexamples, the people identification model controller 310 obtainsdemographic information from the people meter controller 302 to pass tothe media measurement data controller 212. For example, the people metercontroller 302 determines the demographic information corresponding tothe audience members logged into the meter 102 and/or identified by theaudience audio detector 305. In this manner, the example peopleidentification model controller 310 passes the audience composition(e.g., the people count and the demographic information of theidentified panelists), and the time corresponding to the identificationto the media measurement data controller 212 to generate exposure data.The example people identification model controller 310 is described infurther detail below in connection with FIG. 5 .

The example model database 312 of the illustrated example of FIG. 3stores people identification models generated by the peopleidentification model controller 310. For example, the model database 312may periodically or a-periodically receive new, updated, and/or trainedpeople identification models. In some examples, the model database 312stores one people identification model. In some examples, the modeldatabase 312 stores multiple people identification models. The examplemodel database 312 is utilized for subsequent retrieval by the peoplemeter controller 302, the audience audio detector 305, and/or the peopleidentification model controller 310.

FIG. 4 is a block diagram illustrating an example implementation of theaudience audio detector 305 of FIG. 3 . The example audience audiodetector 305 of FIG. 3 includes an example audio database 402, anexample speech pattern determination controller 404, an example samplingcontroller 406, an example speech pattern counter 408, and an examplespeech pattern identifier 410.

The example audio database 402 of the illustrated example of FIG. 4stores samples of audience audio data and/or signatures obtained fromthe example interface 304 of FIG. 3 . For example, the audio database402 stores the samples of audience audio data captured by the exampleaudio sensor 120 and/or signatures generated by the example mediaidentifier 204 for current and/or subsequent use by the speech patterndetermination controller 404. In some examples, the audio database 402stores tagged and/or analyzed samples of audience audio data and/orsignatures.

The example speech pattern determination controller 404 of theillustrated example of FIG. 4 obtains samples of audio data and/orsignatures from the example audio database 402. The example speechpattern determination controller 404 analyzes the samples and/orsignatures for speech patterns. In an example operation, the speechpattern determination controller 404 identifies portions of the audiodata that warrant further attention. For example, the speech patterndetermination controller 404 filters the samples for human voices. Thatis, the speech pattern determination controller applies a low passfilter, a high pass filter, a bandpass filter, etc. to removefrequencies incompatible with human speech (e.g., frequenciescorresponding to a dog barking, a police siren, etc.).

In some examples, the speech pattern determination controller 404 doesnot detect speech patterns in the sample. In such an example, the speechpattern determination controller 404 may send a trigger to the samplingcontroller 406 to prompt the sampling controller 406 to recordadditional samples of audio data of the media presentation environment104. For example, the audience audio detector 305 may be initiated toidentify audience member speech patterns when the media device 110 isturned on. In such examples, when the media device 110 is turned on, anaudience is generally present. If the example speech patterndetermination controller 404 does not detect speech patterns, then arecapture of the environment is to occur. For example, an audiencemember may have briefly left the room after turning on the media device110, the audio sensor 120 may have not captured the media presentationenvironment 104 when the media device 110 was turned on, etc.

In some examples, the speech pattern determination controller 404provides the evaluation results to the example people identificationmodel controller 310 (FIG. 3 ). The example people identification modelcontroller 310 utilizes the filtered output and/or evaluation results astraining data for predicting a people count of the media presentationenvironment 104.

The example speech pattern counter 408 of the illustrated example ofFIG. 4 obtains the filtered output from the speech pattern determinationcontroller 404 corresponding to a number, if any, of speech patterns.For example, the speech pattern determination controller 404 providesinformation indicative of the evaluation of the signatures to the speechpattern counter 408. In examples disclosed herein, the speech patterncounter 408 determines a number of distinct speech patterns detected inthe filtered output. For example, the speech pattern counter 408 mayanalyze the filtered output to determine the number of distinct speechpatterns based on one or more characteristics of the signatures. Forexample, the characteristics can include vocal tract length, vocal tractshape, pitch, speaking rate, etc. Additionally or alternatively, thespeech pattern counter 408 can identify unique speech patterns based ona number of matches corresponding to different reference audiencesignatures that match the generated signature during a monitoringinterval. For example, the monitoring interval can be 30 seconds. Insome examples, the monitoring interval is greater than or less than 30seconds. The example speech pattern counter 408 determines the number ofdifferent reference audience signature sets (e.g., corresponding todifferent audience members) during the monitoring interval. In such anexample, the number of matches during the monitoring interval is thenumber of identified unique speech patterns (e.g., the people count).

The example speech pattern counter 408 includes a counter, such as adevice which stores a number of times a distinct characteristiccorresponds to a speech pattern. If the example speech pattern counter408 determines that speech patterns were detected, then the examplespeech pattern counter 408 increments the counter to the number ofspeech patterns that were detected. For example, if the speech patterncounter 408 detected five speech patterns, the speech pattern counter408 stores a count of five speech patterns. If the example speechpattern counter 408 does not receive information indicative of adetection of speech patterns, then the example speech pattern counter408 updates the speech pattern count with a count of zero. In someexamples disclosed herein, the speech pattern count is the people count(e.g., each detected speech pattern corresponds to a person).

In some examples, the speech pattern counter 408 tags the sample ofaudio data and/or signatures with the people count. For example, thesample includes metadata indicative of a time the sample was recorded,the size of the sample, the device that captured the sample, etc., andfurther includes the people count appended by the speech pattern counter408. In some examples, the tagged sample is stored in the audio database402 and/or provided to the example comparator 308 and the example peopleidentification model controller 310.

The example speech pattern identifier 410 of the illustrated example ofFIG. 4 obtains the filtered output from the speech pattern determinationcontroller 404 corresponding to a number, if any, of speech patterns.For example, the speech pattern determination controller 404 providesinformation indicative of the evaluation of the sample of audio dataand/or signatures to the speech pattern identifier 410. The examplespeech pattern identifier 410 includes audience data, such as referenceaudience signatures of the speech patterns of the audience members(e.g., panelists) of the household (e.g., the panelists 106, 107, 108 ofFIG. 1 ). That is, the speech pattern identifier 410 includes referencespeech patterns generated during panelist registration. If the examplespeech pattern identifier 410 determines speech patterns were detected,the example speech pattern identifier 410 compares the signaturesincluding the detected speech patterns to the reference speech patternsignatures. For example, the speech pattern identifier 410 compares theone or more characteristics of the detected speech patterns and thereference speech patterns to determine a match. If the speech patternidentifier 410 determines a match, the speech pattern identifier 410logs the corresponding audience member as being identified. In someexamples, if the speech pattern identifier 410 determines the detectedspeech pattern does not match any of the stored audience speechpatterns, the speech pattern identifier 410 compares the generatedsignature of the detected speech pattern to reference signatures storedin the example reference signature database 412 corresponding to media(e.g., television shows, movies, etc.). That is, the speech patternidentifier 410 determines whether the detected speech patterncorresponds to a speaker in a media program rather than an audiencemember. If the speech pattern identifier 410 determines the detectedspeech pattern does not match any of the stored audience member speechpatterns (e.g., reference audience signatures) or reference signatures,the speech pattern identifier 410 may add the respective speech to theaudience audio data.

In some examples, the speech pattern identifier 410 determines whetherthe number of identified audience members matches the number of detectedspeech patterns. For example, the speech pattern identifier 410 comparesthe number of identified audience members to the number of detectedspeech patterns. In some examples, the speech pattern identifier 410determines there are more detected speech patterns than identifiedaudience members. In such an example, the audio sensor 120 may haverecorded audio corresponding to the voice of a visitor who has not beenregistered with the meter 102. If the speech pattern identifier 410determines the number of identified audience members does not match thenumber of detected speech patterns, the example people meter controller302 initiates a prompt to the audience. For example, the people metercontroller 302 generates a prompting message requesting the audienceidentify themselves (e.g., enter respective panelist numbers, visitornumbers, etc.).

If the example speech pattern identifier 410 determines the number ofidentified audience members matches the number of detected speechpatterns, the example speech pattern identifier 410 determines a time ofthe identification. For example, the speech pattern identifier 410identifies the timestamp of when the match between the identified speechpattern and the reference speech pattern was determined.

In some examples, the speech pattern identifier 410 tags the sample ofaudio data and/or signatures with the audience member identity (e.g.,assigned panelist number, visitor number, etc.). For example, the sampleincludes metadata indicative of a time the sample was recorded, the sizeof the sample, the device that captured the sample, etc., and furtherincludes the one or more audience member identities identified by thespeech pattern identifier 410. In some examples, the tagged sample isstored in the audio database 402 and/or provided to the example peoplemeter controller 302 and the example people identification modelcontroller 310.

In some examples, the audio database 402, the speech patterndetermination controller 404, the sampling controller 406, the speechpattern counter 408, the speech pattern identifier 410 and/or otherwisethe audience audio detector 305 may be coupled to the peopleidentification model controller 310 of FIG. 3 in an effort to providetraining data (e.g., people monitoring data) to the peopleidentification model. In some examples, it is beneficial to providetraining data (e.g., people monitoring data) to the peopleidentification model controller 310 to train the people identificationmodel to predict audience composition at particular times throughout theday.

Turning to FIG. 5 , a block diagram illustrating the example peopleidentification model controller 310 of FIG. 3 is depicted to train amodel to learn about the presence of an audience of a household mediapresentation environment. The example people identification modelcontroller 310 includes an example communication controller 502, anexample feature extractor 504, an example model trainer 506, an examplemodel updater 508, and an example model generator 510.

The example communication controller 502 of the illustrated example ofFIG. 5 obtains information from the example audience audio detector 305of FIG. 3 to pass to the example media measurement data controller 212of FIG. 2 . The example communication controller 502 is communicativelycoupled to the example audience audio detector 305, the example mediameasurement data controller 212, and the example feature extractor 504.In some examples, the communication controller 502 provides the audiencecomposition to the media measurement data controller 212. For example,the audience audio detector 305 provides an updated and/or accurateaudience composition (e.g., panelists 106, 107, 108) viewing media inthe media presentation environment 104. An updated and/or accurateaudience composition is a verified audience composition based on theaudience member identification from the audience audio detector 305 ofFIG. 3 . In some examples, the communication controller 502 obtainsdemographic data from the people meter controller 302, indicative of thedemographics of the people in the media presentation environment 104,and provides the demographic data to the media measurement datacontroller 212.

The example feature extractor 504 of the illustrated example of FIG. 5extracts features from information obtained from the example audienceaudio detector 305 of FIG. 3 , the example people meter controller 302of FIG. 3 , and the media measurement data controller 212 of FIG. 2 .For example, the feature extractor 504 obtains people monitoring data,over time, from one or more of the audience audio detector 305, thepeople meter controller 302, and the media measurement data controller212, and generates a feature vector corresponding to the peoplemonitoring data. In some examples, the feature extractor 504 obtainsmultiple instances of people monitoring data before generating a featurevector. For example, the feature extractor 504 obtains people monitoringdata over the span of a week. The example feature extractor 504generates or builds derived values of feature vectors (e.g.,representative of features in the people monitoring data) that are to beinformative and non-redundant to facilitate the training phase of thepeople identification model controller 310. As used herein, a featurevector is an n-dimensional array (e.g., a vector) of features thatrepresent some physical environment, media display, measurementparameter, etc. For example, a feature vector represents descriptivecharacteristics of the media presentation environment at a particulardate and time.

In the illustrated example of FIG. 5 , the feature vector can determinethe number of people and/or the identity of the people accounted for inthe media presentation environment 104 in addition to the time at whichthey were accounted, and the media displayed at the time for which theywere accounted. The feature vector provided by the feature extractor 504facilitates the model trainer 506 in training a people identificationmodel to determine an audience composition for a time and a media type.For example, at time t1 on date X in the media presentation environment104, the feature extractor 504 extracts data indicative of mediaidentifying information for a broadcast of “ABC the Bachelor,” as wellas data indicative that three identified audience members are viewingthe broadcast.

In some examples, these extracted features, by themselves, may havelimited usefulness, because there is just one such feature event in agiven instance of people monitoring data. However, if the featureextractor 504 extracts feature data from multiple instances of peoplemonitoring data, the generated feature vector may be sufficient to trainthe people identification model. For example, the feature extractor 504extracts feature data having date X, Y, and Z at the time t1, indicativeof media identifying information for the broadcast of “ABC the Bachelor”and indicative that three identified audience members are viewing thebroadcast. In such an example, the model trainer 506 can utilize thefeature vector to train the people identification model to predict theaudience composition for time t1.

The example model trainer 506 of the illustrated example of FIG. 5trains the people identification model based on the output featurevector of the feature extractor 504. The model trainer 506 operates in atraining mode where it receives multiple instances of people monitoringdata, generates a prediction, and outputs a people identification modelbased on that prediction. For the example model trainer 506 generates apeople identification model, the model trainer 506 receives featurevectors corresponding to actual representations of the mediapresentation environment 104. For example, during a training mode,verifications are made about the audience composition of the mediapresentation environment 104 so that the data they provide to theaudience audio detector 305 is suitable for learning. For example, themodel trainer 506 receives a feature vector indicative of the featuresof an actual media presentation environment and identifies a pattern inthe features that maps the dates and times of the actual mediapresentation environment to the audience composition and outputs a modelthat captures these daily and/or weekly patterns. The example modeltrainer 506 provides the output people identification model to theexample model updater 508 to assist in generating predictions about theaudience composition at subsequent dates and times.

The example model updater 508 of the illustrated example of FIG. 5 flagsa people identification model received from the model trainer 506 as newand/or updated. For example, the model updater 508 can receive a peopleidentification model from the model trainer 506 that provides aprediction algorithm to determine an audience composition of people inthe media presentation environment 104. The model updater 508 determinesthat a people identification model of this type is new and, therefore,tags it as new. In some examples, the model updater 508 determines thata people identification model of this type has been generated previouslyand, therefore, will flag the model most recently generated as updated.The example model updater 508 provides the new and/or updated model tothe model generator 510.

The example model generator 510 of the illustrated example of FIG. 5generates a people identification model for publishing. For example, themodel generator 510 may receive a notification from the model updater508 that a new and/or updated people identification model has beentrained and the model generator 510 may create a file in which thepeople identification model is published so that the peopleidentification model can be saved and/or stored as the file. In someexamples, the model generator 510 provides a notification to the peoplemeter controller 302 and/or the audience audio detector 305 that apeople identification model is ready to be transformed and published. Insome examples, the model generator 510 stores the people identificationmodel in the example model database 312 for subsequent retrieval by thepeople meter controller 302.

In some examples, the people identification model controller 310determines a people identification model is trained and ready for usewhen the prediction meets a threshold amount of error. In some examples,the people meter controller 302 and/or audience audio detector 305implements the trained people identification model to determine anaudience composition of people in a media presentation environment. Insome examples, the example people meter 210 implements the peopleidentification model. In such an example, the people identificationmodel would obtain audio data from the audio sensor 120 to make informeddecisions about audience composition, without the use of audience inputdata. In this manner, the people identification model may augment orreplace the people meter controller 302, the audience audio detector305, and the comparator 308.

While example manners of implementing the people meter 210 of FIG. 2 isillustrated in FIGS. 3, 4, and 5 , one or more of the elements,processes and/or devices illustrated in FIGS. 3, 4, and 5 may becombined, divided, re-arranged, omitted, eliminated and/or implementedin any other way. Further, the example people meter controller 302, theexample interface 304, the example audience audio detector 305, theexample comparator 308, the example people identification modelcontroller 310, the example model database 312, the example audiodatabase 402, the example speech pattern determination controller 404,the example sampling controller 406, the example speech pattern counter408, the example speech pattern identifier 410, the example referencesignature database 412, the example communication controller 502, theexample feature extractor 504, the example model trainer 506, theexample model updater 508, the example model generator 510 and/or, moregenerally, the example people meter 210 of FIG. 2 may be implemented byhardware, software, firmware and/or any combination of hardware,software and/or firmware. Thus, for example, any of the example peoplemeter controller 302, the example interface 304, the example audienceaudio detector 305, the example comparator 308, the example peopleidentification model controller 310, the example model database 312, theexample audio database 402, the example speech pattern determinationcontroller 404, the example sampling controller 406, the example speechpattern counter 408, the example speech pattern identifier 410, theexample reference signature database 412, the example communicationcontroller 502, the example feature extractor 504, the example modeltrainer 506, the example model updater 508, the example model generator510 and/or, more generally, the example people meter 210 could beimplemented by one or more analog or digital circuit(s), logic circuits,programmable processor(s), programmable controller(s), graphicsprocessing unit(s) (GPU(s)), digital signal processor(s) (DSP(s)),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)) and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example, people meter controller 302, the example interface 304, theexample audience audio detector 305, the example comparator 308, theexample people identification model controller 310, the example modeldatabase 312, the example audio database 402, the example speech patterndetermination controller 404, the example sampling controller 406, theexample speech pattern counter 408, the example speech patternidentifier 410, the example reference signature database 412, theexample communication controller 502, the example feature extractor 504,the example model trainer 506, the example model updater 508, theexample model generator 510 is/are hereby expressly defined to include anon-transitory computer readable storage device or storage disk such asa memory, a digital versatile disk (DVD), a compact disk (CD), a Blu-raydisk, etc. including the software and/or firmware. Further still, theexample people meter of FIG. 2 may include one or more elements,processes and/or devices in addition to, or instead of, thoseillustrated in FIGS. 3, 4, and 5, and/or may include more than one ofany or all of the illustrated elements, processes and devices. As usedherein, the phrase “in communication,” including variations thereof,encompasses direct communication and/or indirect communication throughone or more intermediary components, and does not require directphysical (e.g., wired) communication and/or constant communication, butrather additionally includes selective communication at periodicintervals, scheduled intervals, aperiodic intervals, and/or one-timeevents.

A flowchart representative of example hardware logic, machine readableinstructions, hardware implemented state machines, and/or anycombination thereof for implementing the people meter 210 of FIGS. 3, 4,and 5 are shown in FIGS. 6-8 . The machine readable instructions may beone or more executable programs or portion(s) of an executable programfor execution by a computer processor and/or processor circuitry, suchas the processor 912 shown in the example processor platform 900discussed below in connection with FIGS. 6-8 . The program may beembodied in software stored on a non-transitory computer readablestorage medium such as a CD-ROM, a floppy disk, a hard drive, a DVD, aBlu-ray disk, or a memory associated with the processor 912, but theentire program and/or parts thereof could alternatively be executed by adevice other than the processor 912 and/or embodied in firmware ordedicated hardware. Further, although the example programs are describedwith reference to the flowcharts illustrated in FIGS. 6-9 , many othermethods of implementing the example people meter 210 may alternativelybe used. For example, the order of execution of the blocks may bechanged, and/or some of the blocks described may be changed, eliminated,or combined. Additionally or alternatively, any or all of the blocks maybe implemented by one or more hardware circuits (e.g., discrete and/orintegrated analog and/or digital circuitry, an FPGA, an ASIC, acomparator, an operational-amplifier (op-amp), a logic circuit, etc.)structured to perform the corresponding operation without executingsoftware or firmware. The processor circuitry may be distributed indifferent network locations and/or local to one or more devices (e.g., amulti-core processor in a single machine, multiple processorsdistributed across a server rack, etc).

The machine readable instructions described herein may be stored in oneor more of a compressed format, an encrypted format, a fragmentedformat, a compiled format, an executable format, a packaged format, etc.Machine readable instructions as described herein may be stored as dataor a data structure (e.g., portions of instructions, code,representations of code, etc.) that may be utilized to create,manufacture, and/or produce machine executable instructions. Forexample, the machine readable instructions may be fragmented and storedon one or more storage devices and/or computing devices (e.g., servers)located at the same or different locations of a network or collection ofnetworks (e.g., in the cloud, in edge devices, etc.). The machinereadable instructions may require one or more of installation,modification, adaptation, updating, combining, supplementing,configuring, decryption, decompression, unpacking, distribution,reassignment, compilation, etc. in order to make them directly readable,interpretable, and/or executable by a computing device and/or othermachine. For example, the machine readable instructions may be stored inmultiple parts, which are individually compressed, encrypted, and storedon separate computing devices, wherein the parts when decrypted,decompressed, and combined form a set of executable instructions thatimplement one or more functions that may together form a program such asthat described herein.

In another example, the machine readable instructions may be stored in astate in which they may be read by processor circuitry, but requireaddition of a library (e.g., a dynamic link library (DLL)), a softwaredevelopment kit (SDK), an application programming interface (API), etc.in order to execute the instructions on a particular computing device orother device. In another example, the machine readable instructions mayneed to be configured (e.g., settings stored, data input, networkaddresses recorded, etc.) before the machine readable instructionsand/or the corresponding program(s) can be executed in whole or in part.Thus, machine readable media, as used herein, may include machinereadable instructions and/or program(s) regardless of the particularformat or state of the machine readable instructions and/or program(s)when stored or otherwise at rest or in transit.

The machine readable instructions described herein can be represented byany past, present, or future instruction language, scripting language,programming language, etc. For example, the machine readableinstructions may be represented using any of the following languages: C,C++, Java, C #, Perl, Python, JavaScript, HyperText Markup Language(HTML), Structured Query Language (SQL), Swift, etc.

As mentioned above, the example processes of FIGS. 6-9 may beimplemented using executable instructions (e.g., computer and/or machinereadable instructions) stored on a non-transitory computer and/ormachine readable medium such as a hard disk drive, a flash memory, aread-only memory, a compact disk, a digital versatile disk, a cache, arandom-access memory and/or any other storage device or storage disk inwhich information is stored for any duration (e.g., for extended timeperiods, permanently, for brief instances, for temporarily buffering,and/or for caching of the information). As used herein, the termnon-transitory computer readable medium is expressly defined to includeany type of computer readable storage device and/or storage disk and toexclude propagating signals and to exclude transmission media.

“Including” and “comprising” (and all forms and tenses thereof) are usedherein to be open ended terms. Thus, whenever a claim employs any formof “include” or “comprise” (e.g., comprises, includes, comprising,including, having, etc.) as a preamble or within a claim recitation ofany kind, it is to be understood that additional elements, terms, etc.may be present without falling outside the scope of the correspondingclaim or recitation. As used herein, when the phrase “at least” is usedas the transition term in, for example, a preamble of a claim, it isopen-ended in the same manner as the term “comprising” and “including”are open ended. The term “and/or” when used, for example, in a form suchas A, B, and/or C refers to any combination or subset of A, B, C such as(1) A alone, (2) B alone, (3) C alone, (4) A with B, (5) A with C, (6) Bwith C, and (7) A with B and with C. As used herein in the context ofdescribing structures, components, items, objects and/or things, thephrase “at least one of A and B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. Similarly, as used herein in the contextof describing structures, components, items, objects and/or things, thephrase “at least one of A or B” is intended to refer to implementationsincluding any of (1) at least one A, (2) at least one B, and (3) atleast one A and at least one B. As used herein in the context ofdescribing the performance or execution of processes, instructions,actions, activities and/or steps, the phrase “at least one of A and B”is intended to refer to implementations including any of (1) at leastone A, (2) at least one B, and (3) at least one A and at least one B.Similarly, as used herein in the context of describing the performanceor execution of processes, instructions, actions, activities and/orsteps, the phrase “at least one of A or B” is intended to refer toimplementations including any of (1) at least one A, (2) at least one B,and (3) at least one A and at least one B.

As used herein, singular references (e.g., “a”, “an”, “first”, “second”,etc.) do not exclude a plurality. The term “a” or “an” entity, as usedherein, refers to one or more of that entity. The terms “a” (or “an”),“one or more”, and “at least one” can be used interchangeably herein.Furthermore, although individually listed, a plurality of means,elements or method actions may be implemented by, e.g., a single unit orprocessor. Additionally, although individual features may be included indifferent examples or claims, these may possibly be combined, and theinclusion in different examples or claims does not imply that acombination of features is not feasible and/or advantageous.

FIGS. 6, 7, and 8 illustrate programs that are executed by the examplepeople meter 210 to determine an accurate audience composition utilizingaudience input and/or audio data. FIG. 6 illustrates an example program600 implemented by the example audience audio detector 305 of FIGS. 3and/or 4 to determine a people count. FIG. 7 illustrates an exampleprogram 700 implemented by the example people meter 210 of FIGS. 2, 3, 4, and/or 5 to verify an audience composition of the media presentationenvironment. FIG. 8 illustrates an example program 800 implemented bythe example people identification model controller 310 of FIGS. 3 and/or5 to train the model to determine an audience composition of the mediapresentation environment.

Turning to FIG. 6 , the example program 600 begins when the examplemedia device 110 (FIG. 1 ) is on (block 602). For example, the audiosensor 120 (FIG. 1 ) may be activated and begin capturing audio datawhen the media device 110 is turned on, and thus the program 600 begins.The example media identifier 204 (FIG. 2 ) obtains samples of audio datafrom the example audio sensor 120 (block 604). For example, the audiosensor 120 captures samples of audio data corresponding to the mediapresentation environment 104 and the media identifier 204 generates oneor more signatures based on the audio data.

The speech pattern determination controller 404 (FIG. 4 ) obtains one ormore signatures from the media identifier 204 (block 606). For example,the audio database 402 obtains a signature via the interface 304 (FIG. 3) and the speech pattern determination controller 404 queries the audiodatabase 402 for signatures corresponding to the media presentationenvironment 104.

The example speech pattern determination controller 404 applies filtersto the signature(s) corresponding to the media presentation environment104 (block 608). For example, the speech pattern determinationcontroller 404 applies a low pass filter, a high pass filter, a bandpassfilter, etc. to remove frequencies that are not associated with humanvoice (e.g., frequencies associated with animals, frequencies associatedwith cars, etc.). The filtered output is indicative of signature(s) ofhuman voices detected during the filtering. In this manner, the examplespeech pattern determination controller 404 evaluates the filteredoutput (block 610). For example, the speech pattern determinationcontroller 404 utilizes the information in the filtered output topredict and/or otherwise determine audience member identities.

The example speech pattern determination controller 404 determines ifspeech patterns were detected (block 612). For example, the speechpattern determination controller 404 utilizes the evaluation of thefiltered output to determine if speech patterns were detected based onone or more characteristics (e.g., vocal tract length, vocal tractshape, pitch, speaking rate, etc.). If the example speech patterndetermination controller 404 determines speech patterns were notdetected (e.g., block 612=NO), then the example speech patterndetermination controller 404 prompts the sampling controller 406 tosample the media environment (block 614). For example, the speechpattern determination controller 404 may send a trigger to the samplingcontroller 406 to prompt the sampling controller 406 to recordadditional samples of audio data of the media presentation environment104.

If the example speech pattern determination controller 404 determinesspeech patterns were detected (e.g., block 612=YES), then the examplespeech pattern determination controller 404 provides the filtered outputto the example speech pattern counter 408. The example speech patterncounter 408 determines the number of speech patterns in the filteredoutput (block 616). That is, the speech pattern counter 408 determinesthe people count of the audience. For example, the speech patterncounter 408 analyzes information in the filtered output to determine thenumber of speech patterns that were identified in the signatures. Insome examples, the speech pattern counter 408 updates the counter withthe number of speech patterns (e.g., the speech pattern counter 408increments the counter to equal the number of speech patterns detectedin the signatures).

The example speech pattern counter 408 provides the people count to thecomparator 308 (FIG. 3 ) (block 618). For example, the speech patterncounter 408 is communicatively coupled to the comparator 308, andfurther provides the people count to the comparator 308 for a comparisonto the prompted people count and/or the previously stored people count.The program 600 ends when the example speech pattern counter 408provides the people count to the comparator 308. The program 600 repeatswhen the example sampling controller 406 (FIG. 4 ) initiates a newsample of audio data. For example, the sampling controller 406 mayinitiate the audio sensor 120 to record audio samples of the mediapresentation environment 104, and thus a new and/or same audience memberidentification, in the media presentation environment 104, may bedetected.

Turning to FIG. 7 , the example program 700 begins when the examplecomparator 308 (FIG. 3 ) obtains the people (block 702). For example,the comparator 308 obtains the people count from the audience audiodetector 305 (FIG. 3 ) via the interface 304 (FIG. 3 ).

The example comparator 308 determines if the people count has changed(block 704). For example, the comparator 308 compares the people countto a previously stored people count determined at a previous sampling ofthe media presentation environment 104. In some examples, there is noprevious sample of the media presentation environment 104 (e.g., themedia device 110 was off). In such an example, the previously storedpeople count is zero. If the example comparator 308 detects no change inthe people count (e.g., block 704=NO), the example comparator 308determines a time of the comparison (block 712). If the examplecomparator 308 determines a change in people count (e.g., block704=YES), the example speech pattern identifier 410 identifies one ormore audience members associated with the identified speech patterns ofthe signatures (block 706). For example, the speech pattern identifier410 compares the detected speech patterns to one or more referencespeech patterns of audience members of the household.

The example speech pattern identifier 410 determines whether the numberof identified audience members matches the number of detected speechpatterns (block 708). For example, the speech pattern identifier 410compares the number of identified audience members to the number ofdetected speech patterns. If the speech pattern identifier 410determines the number of identified audience members does not match thenumber of detected speech patterns (e.g., block 708=NO), the examplepeople meter controller 302 initiates a prompt to the audience (block710). For example, the people meter controller 302 generates a promptingmessage. In this manner, the example people meter controller 302generates the prompting message in an effort to obtain a response fromthe audience members to identify the unidentified humans represented bythe signatures (e.g., the humans the example speech pattern identifier410 did not identify) and generate accurate audience monitoring data.

If the example speech pattern identifier 410 determines the number ofidentified audience members matches the number of detected speechpatterns (e.g., block 708=YES), the example speech pattern identifier410 determines a time of the identification (block 712). For example,the speech pattern identifier 410 identifies the timestamp of when thematch between the identified speech pattern and the reference speechpattern was determined. In some examples, the speech pattern identifier410 identifies the timestamp of receiving the audience member identityin response to the prompt.

The example speech pattern determination controller 404 provides theaudience composition (e.g., the people count, the audience members inthe audience, etc.) and the time to the people identification model(block 714). For example, the speech pattern determination controller404 outputs information determined from the comparison to train thepeople identification model. Further, the example audience audiodetector 305 sends a reset notification to the example people metercontroller 302 (block 716). For example, the audience audio detector 305notifies the example people meter controller 302 to reset the schedulinginterval timers that determine when prompting messages are to betriggered. In some examples, when the audience audio detector 305provides the notification to the people meter controller 302, theexample program 700 ends.

FIG. 8 illustrates an example training program 800 to train a peopleidentification model to predict a verified audience composition forsubsequent dates and times in the media presentation environment 104.The example machine readable instructions 800 may be used to implementthe example people identification model controller 310 of FIG. 3 . InFIG. 8 , the example training program 800 beings at block 802, when theexample feature extractor 504 (FIG. 5 ) obtains comparison data from theexample comparator 308 (FIG. 3 ). For example, the comparator 308provides comparison results and time stamps to the example featureextractor 504.

The example feature extractor 504 obtains data from the example peoplemeter controller 302 (FIG. 3 ) (block 804). For example, the peoplemeter controller 302 provides demographic data corresponding to thelogged in audience member members at a time they logged in. The examplefeature extractor 504 obtains evaluation data from the example audienceaudio detector 305 (FIG. 3 ) (block 806). For example, the audienceaudio detector 305 provides the analysis and evaluation results (e.g.,people identification data) of the signatures for a particular time.Additionally, the example audience audio detector 305 provides thetagged sample (e.g., the sample tagged with a people count by the speechpattern counter 408 (FIG. 4 ) and/or the audience member identifier bythe speech pattern identifier 410 (FIG. 4 )) to the example featureextractor 504.

The example feature extractor 504 obtains media identifying informationfrom the example media measurement data controller 212 (FIG. 2 ) (block808). For example, the media measurement data controller 212 providesmedia identifying information to the communication controller 502 (FIG.5 ) in response to receiving a speech pattern count and/or panelistidentifier(s), and the communication controller 502 provides the mediaidentifying information to the feature extractor 504.

The example feature extractor 504 extracts features of the peoplemonitoring information (block 810). As used herein, the peoplemonitoring information corresponds to the information and data obtainedfrom the example people meter controller 302, the example comparator308, the example audience audio detector 305, and the example mediameasurement data controller 212. This data can be used to determine averified audience composition and/or represents a verified audiencecomposition.

The example feature extractor 504 generates a feature vectorcorresponding to the extracted features of the people monitoring data(block 812). For example, the feature extractor 504 generates a featurevector that represents descriptive characteristics of a physicalenvironment (e.g., the media presentation environment) at particulardates and times, or at a particular date and time. The example featureextractor 504 determines if there are additional people monitoring data(block 814). For example, the feature extractor 504 determines ifanother set of people monitoring data, representative of the peoplecount during a different time in the media presentation environment 104,is available. If the example feature extractor 504 determines there areadditional people monitoring data (block 814=YES), then control returnsto block 802. In such an example, the model trainer 506 (FIG. 5 ) needsto receive people monitoring data of the media presentation environment104 that is sufficient to generate a sufficiently accurate and/orprecise model.

If the example feature extractor 504 determines there are not additionalpeople monitoring data (block 814=NO), then the example model trainer506 trains the people identification model based on the feature vector(block 816). For example, the model trainer 506 may utilize a machinelearning technique to predict output probability values corresponding tothe number of people and/or which audience members are in the mediapresentation environment 104. The output probability values couldcorrespond to future predictions of the audience members viewingparticular media in the media presentation environment 104 or the outputprobability values could correspond to future predictions of theaudience members in the media presentation environment 104 at aparticular hour of the day or day of the week.

After the people identification model has been trained, the examplemodel updater 508 flags the people identification model as new orupdated. Further, the example model generator 510 generates the trainedmodel (block 818). For example, the model generator 510 receives the newand/or updated trained people identification model from the modelupdater 508 and generates a file to store/save the trained peopleidentification model for subsequent access by the people metercontroller 302 (FIG. 3 ) and/or the audience audio detector 305 (FIG. 3).

The example model generator 510 stores the trained people identificationmodel in the example model database 312 (FIG. 3 ) (block 820). Thetraining program 800 ends when the trained people identification modelis stored in the example model database 312. The training program 800repeats when the example feature extractor 504 obtains people monitoringdata.

In some examples, the trained people identification model is publishedby the people identification model controller 310. When the peopleidentification model is published, the people identification modeloperates in a detection phase, where the example people identificationmodel controller 310 utilizes the trained model, in real time, todetermine an accurate audience composition of the media presentationenvironment 104. In some examples, the people identification modelreplaces the people meter controller 302, the audience audio detector305, and the comparator 308. In such an example, the peopleidentification model obtains input data from the audio sensor 120 todetermine an accurate audience composition of the media presentationenvironment 104. Such input from the audio sensor 120 includes samplesof audio data. For example, the people identification model utilizes itsprediction capabilities in connection with information obtained aboutthe media presentation environment 104 to output an accuraterepresentation of the number and/or identification of people in themedia presentation environment 104. In such an example, the people metercontroller 302 no longer requires audience input, and thus compliancebecomes less of an issue when determining an accurate audiencecomposition.

FIG. 9 is a block diagram of an example processor platform 900structured to execute the instructions of FIGS. 6-8 to implement thepeople meter 210 of FIGS. 2-5 . The processor platform 900 can be, forexample, a server, a personal computer, a workstation, a self-learningmachine (e.g., a neural network), a mobile device (e.g., a cell phone, asmart phone, a tablet such as an iPad′), a personal digital assistant(PDA), an Internet appliance, a DVD player, a CD player, a digital videorecorder, a Blu-ray player, a gaming console, a personal video recorder,a set top box, a headset or other wearable device, or any other type ofcomputing device.

The processor platform 900 of the illustrated example includes aprocessor 912. The processor 912 of the illustrated example is hardware.For example, the processor 912 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors, GPUs, DSPs, orcontrollers from any desired family or manufacturer. The hardwareprocessor may be a semiconductor based (e.g., silicon based) device. Inthis example, the processor implements the example people metercontroller 302, the example interface 304, the example audience audiodetector 305, the example comparator 308, the example peopleidentification model controller 310, the example model database 312, theexample audio database 402, the example speech pattern determinationcontroller 404, the example sampling controller 406, the example speechpattern counter 408, the example speech pattern identifier 410, theexample reference signature database 412, the example communicationcontroller 502, the example feature extractor 504, the example modeltrainer 506, the example model updater 508, and the example modelgenerator 510.

The processor 912 of the illustrated example includes a local memory 913(e.g., a cache). The processor 912 of the illustrated example is incommunication with a main memory including a volatile memory 914 and anon-volatile memory 916 via a bus 918. The volatile memory 914 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS® Dynamic Random Access Memory(RDRAM®) and/or any other type of random access memory device. Thenon-volatile memory 916 may be implemented by flash memory and/or anyother desired type of memory device. Access to the main memory 914, 916is controlled by a memory controller.

The processor platform 900 of the illustrated example also includes aninterface circuit 920. The interface circuit 920 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), a Bluetooth® interface, a near fieldcommunication (NFC) interface, and/or a PCI express interface.

In the illustrated example, one or more input devices 922 are connectedto the interface circuit 920. The input device(s) 922 permit(s) a userto enter data and/or commands into the processor 912. The inputdevice(s) can be implemented by, for example, an audio sensor, amicrophone, a camera (still or video), a keyboard, a button, a mouse, atouchscreen, a track-pad, a trackball, isopoint and/or a voicerecognition system.

One or more output devices 924 are also connected to the interfacecircuit 920 of the illustrated example. The output devices 1024 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay (LCD), a cathode ray tube display (CRT), an in-place switching(IPS) display, a touchscreen, etc.), a tactile output device, a printerand/or speaker. The interface circuit 920 of the illustrated example,thus, typically includes a graphics driver card, a graphics driver chipand/or a graphics driver processor.

The interface circuit 920 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem, a residential gateway, a wireless access point, and/or a networkinterface to facilitate exchange of data with external machines (e.g.,computing devices of any kind) via a network 926. The communication canbe via, for example, an Ethernet connection, a digital subscriber line(DSL) connection, a telephone line connection, a coaxial cable system, asatellite system, a line-of-site wireless system, a cellular telephonesystem, etc.

The processor platform 900 of the illustrated example also includes oneor more mass storage devices 928 for storing software and/or data.Examples of such mass storage devices 928 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, redundantarray of independent disks (RAID) systems, and digital versatile disk(DVD) drives.

The machine executable instructions 932 of FIGS. 6-8 may be stored inthe mass storage device 928, in the volatile memory 914, in thenon-volatile memory 916, and/or on a removable non-transitory computerreadable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that example methods,apparatus and articles of manufacture have been disclosed that determinean audience composition in a media presentation environment bygenerating signatures from audio data and comparing an evaluation of thesignatures to audience input data. The disclosed example methods,apparatus and articles of manufacture improve the efficiency of using acomputing device by using the audience input data and the evaluation ofthe signatures to train a people identification model to determine theaudience composition. The people identification model, once trained, canreplace the people meter and thus, improve the efficiency processingtime by eliminating a need for audience input data. The disclosedexample methods, apparatus and articles of manufacture improve theefficiency of using a computing device by reducing prompting messageswhen the speech patterns identified based on the signatures of audiodata match the reference speech patterns of audience member audio. Thedisclosed methods, apparatus and articles of manufacture are accordinglydirected to one or more improvement(s) in the functioning of a computer.

Example methods, apparatus, systems, and articles of manufacture todetermine an audience composition in a media environment are disclosedherein. Further examples and combinations thereof include the following:

Example 1 includes an apparatus to measure an audience in a mediaenvironment, the apparatus comprising a controller to cause a peoplemeter to emit a prompt for input of audience identification informationat a first time, and determine a first audience count based on theinput, an audio detector to determine a second audience count based onsignatures generated from audio data captured in the media environment,and a comparator to cause the people meter to not emit the prompt for atleast a first time period after the first time when the first audiencecount is equal to the second audience count.

Example 2 includes the apparatus of example 1, wherein the audiodetector is to identify one or more audience members based oncomparisons of the signatures of the audio data with a library ofreference audience signatures.

Example 3 includes the apparatus of example 2, wherein the library ofreference audience signatures includes sample signatures generated fromspeech associated with the one or more of the audience members.

Example 4 includes the apparatus of example 2, wherein the controller isto cause the people meter to emit the prompt when at least one of (i)the first audience count does not equal the second audience count, or(ii) the input audience identification information does not correspondto the one or more audience members identified based on the comparisonsof the signatures of the audio data with a library of reference audiencesignatures.

Example 5 includes the apparatus of example 1, wherein the input is toinclude unique identifiers assigned to the audience.

Example 6 includes the apparatus of example 1, wherein the comparator isto send a reset notification to a counter, the people meter to emit theprompt when the counter expires.

Example 7 includes the apparatus of example 1, wherein the secondaudience count is based on a number of different speech patternsdetected based on the signatures.

Example 8 includes a non-transitory computer readable storage mediumcomprising instructions that, when executed, cause one or moreprocessors to at least cause a people meter to emit a prompt for inputof audience identification information at a first time, determine afirst audience count based on the input, determine a second audiencecount based on signatures generated from audio data captured in a mediaenvironment, cause the people meter to not emit the prompt for at leasta first time period after the first time when the first audience countis equal to the second audience count.

Example 9 includes the non-transitory computer readable storage mediumof example 8, wherein the instructions, when executed, cause the one ormore processors to identify one or more audience members based oncomparisons of the signatures of the audio data with a library ofreference audience signatures.

Example 10 includes the non-transitory computer readable storage mediumof example 9, wherein the library of reference audience signaturesincludes sample signatures generated from speech associated with the oneor more of the audience members.

Example 11 includes the non-transitory computer readable storage mediumof example 9, wherein the instructions, when executed, cause the one ormore processors to cause the people meter to emit the prompt when atleast one of (i) the first audience count does not equal the secondaudience count, or (ii) the input audience identification informationdoes not correspond to the one or more audience members identified basedon the comparisons of the signatures of the audio data with a library ofreference audience signatures.

Example 12 includes the non-transitory computer readable storage mediumof example 8, wherein the input is to include unique identifiersassigned to the audience.

Example 13 includes the non-transitory computer readable storage mediumof example 8, wherein the instructions, when executed, cause the one ormore processors to send a reset notification to a counter, the peoplemeter to emit the prompt when the counter expires.

Example 14 includes the non-transitory computer readable storage mediumof example 8, wherein the second audience count is based on a number ofdifferent speech patterns detected based on the signatures.

Example 15 includes a method to measure an audience in a mediaenvironment, the method comprising causing a people meter to emit aprompt for input of audience identification information at a first time,determining a first audience count based on the input, determining asecond audience count based on signatures generated from audio datacaptured in the media environment, causing the people meter to not emitthe prompt for at least a first time period after the first time whenthe first audience count is equal to the second audience count.

Example 16 includes the method of example 15, further includingidentifying one or more audience members based on comparisons of thesignatures of the audio data with a library of reference audiencesignatures.

Example 17 includes the method of example 16, wherein the library ofreference audience signatures includes sample signatures generated fromspeech associated with the one or more of the audience members.

Example 18 includes the method of example 16, further including causingthe people meter to emit the prompt when at least one of (i) the firstaudience count does not equal the second audience count, or (ii) theinput audience identification information does not correspond to the oneor more audience members identified based on the comparisons of thesignatures of the audio data with a library of reference audiencesignatures.

Example 19 includes the method of example 15, further including sendinga reset notification to a counter, the people meter to emit the promptwhen the counter expires.

Example 20 includes the method of example 15, wherein the secondaudience count is based on a number of different speech patternsdetected based on the signatures.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

The following claims are hereby incorporated into this DetailedDescription by this reference, with each claim standing on its own as aseparate embodiment of the present disclosure.

1-20. (canceled)
 21. An apparatus comprising: interface circuitry toobtain audio data; machine readable instructions; and programmablecircuitry to execute the machine readable instructions to at least:generate a signature from the audio data, the generated signatureassociated with a first time interval; compare the generated signatureto a library of reference signatures, the reference signaturesassociated with one or more identified audience members; and in responseto a determination that the generated signature is not associated with amatch in the library of reference signatures, cause a meter to emit aprompt for input of audience identification information associated withthe first time interval.
 22. The apparatus of claim 21, wherein thegenerated signature is one of a plurality of generated signaturesassociated with the first time interval, and the programmable circuitryis to: determine a first people count associated with the first timeinterval based on comparison of the generated signatures to the libraryof reference signatures; and determine whether to cause the meter toemit the prompt for input of audience identification information basedon the first people and a second people count associated with a secondtime interval prior to the first time interval.
 23. The apparatus ofclaim 22, wherein the programmable circuitry is to: cause the meter toemit the prompt for input of audience identification information inresponse to the first people count not matching the second people count;and cause the meter to not emit the prompt for input of audienceidentification information in response to the first people countmatching the second people count.
 24. The apparatus of claim 23, whereinto cause the meter to not emit the prompt, the programmable circuitry isto notify the meter to reset a timer for scheduling generation ofprompts.
 25. The apparatus of claim 22, wherein the programmablecircuitry is to determine the first people count based on a number ofmatches of the generated signatures to different reference signaturesassociated with different ones of the identified audience members. 26.The apparatus of claim 21, wherein the programmable circuitry is toinitiate signature generation and comparison in response to a mediadevice being turned on.
 27. The apparatus of claim 21, wherein theprogrammable circuitry is to perform signature generation and comparisonin response to detection of a signal associated with control of a mediadevice.
 28. At least one non-transitory computer readable mediumcomprising computer readable instructions to cause one or moreprocessors to at least: generate a signature from audio data, thegenerated signature associated with a first time interval; compare thegenerated signature to a library of reference signatures, the referencesignatures associated with one or more identified audience members; andin response to a determination that the generated signature is notassociated with a match in the library of reference signatures, cause ameter to emit a prompt for input of audience identification informationassociated with the first time interval.
 29. The at least onenon-transitory computer readable medium of claim 28, wherein thegenerated signature is one of a plurality of generated signaturesassociated with the first time interval, and the instructions are tocause the one or more processors to: determine a first people countassociated with the first time interval based on comparison of thegenerated signatures to the library of reference signatures; anddetermine whether to cause the meter to emit the prompt for input ofaudience identification information based on the first people and asecond people count associated with a second time interval prior to thefirst time interval.
 30. The at least one non-transitory computerreadable medium of claim 29, wherein the instructions are to cause theone or more processors to: cause the meter to emit the prompt for inputof audience identification information in response to the first peoplecount not matching the second people count; and cause the meter to notemit the prompt for input of audience identification information inresponse to the first people count matching the second people count. 31.The at least one non-transitory computer readable medium of claim 30,wherein to cause the meter to not emit the prompt, the instructions areto cause the one or more processors to notify the meter to reset a timerfor scheduling generation of prompts.
 32. The at least onenon-transitory computer readable medium of claim 29, wherein theinstructions are to cause the one or more processors to determine thefirst people count based on a number of matches of the generatedsignatures to different reference signatures associated with differentones of the identified audience members.
 33. The at least onenon-transitory computer readable medium of claim 28, wherein theinstructions are to cause the one or more processors to initiatesignature generation and comparison in response to a media device beingturned on.
 34. The at least one non-transitory computer readable mediumof claim 28, wherein the instructions are to cause the one or moreprocessors to perform signature generation and comparison in response todetection of a signal associated with control of a media device.
 35. Amethod comprising: generating a signature from audio data, the generatedsignature associated with a first time interval; comparing, by executingan instructions with programmable circuitry, the generated signature toa library of reference signatures, the reference signatures associatedwith one or more identified audience members; and in response to adetermination that the generated signature is not associated with amatch in the library of reference signatures, triggering, by executingan instructions with the programmable circuitry, a meter to emit aprompt for input of audience identification information associated withthe first time interval.
 36. The method of claim 35, wherein thegenerated signature is one of a plurality of generated signaturesassociated with the first time interval, and further including:determining a first people count associated with the first time intervalbased on comparison of the generated signatures to the library ofreference signatures; and determining whether to trigger the meter toemit the prompt for input of audience identification information basedon the first people and a second people count associated with a secondtime interval prior to the first time interval.
 37. The method of claim36, further including: triggering the meter to emit the prompt for inputof audience identification information in response to the first peoplecount not matching the second people count; and causing the meter to notemit the prompt for input of audience identification information inresponse to the first people count matching the second people count. 38.The method of claim 37, wherein the causing of the meter to not emit theprompt includes notifying the meter to reset a timer for schedulinggeneration of prompts.
 39. The method of claim 36, wherein thedetermining of the first people count is based on a number of matches ofthe generated signatures to different reference signatures associatedwith different ones of the identified audience members.
 40. The methodof claim 35, further including initiating signature generation andcomparison in response to a media device being turned on.